Causality: Models, Reasoning, and Inference, Judea Pearl, 2009 (Cambridge University Press) - A foundational textbook for understanding causal graphs (DAGs), structural causal models (SCMs), interventions, and counterfactuals, which are essential for causality-aware model development.
Deep Structural Causal Models for Causal Inference and Prediction, Hyunseok Seo, Masoud Badiei Khuzani, Varun Vasudevan, Charles Huang, Hongyi Ren, Ruoxiu Xiao, Xiao Jia, Lei Xing, 2019Medical PhysicsDOI: 10.1002/mp.13649 - Discusses the framework and application of deep learning models designed to directly represent and learn Structural Causal Models, providing a basis for SEM-inspired architectures.